influence.predict.textmodel_affinity: Compute feature influence from a predicted textmodel_affinity...

In quanteda.textmodels: Scaling Models and Classifiers for Textual Data

Compute feature influence from a predicted textmodel_affinity object

Description

Computes the influence of features on scaled textmodel_affinity() applications.

Usage

## S3 method for class 'predict.textmodel_affinity'
influence(model, subset = !train, ...)

Arguments

model	a predicted textmodel_affinity() object
subset	whether to use all data or a subset (for instance, exclude the training set)
...	unused

Value

a named list classed as influence.predict.textmodel_affinity that contains

• norm a document by feature class sparse matrix of normalised influence measures

• count a vector of counts of each non-zero feature in the input matrix

• rate the normalised feature count for each non-zero feature in the input matrix

• mode an integer vector of 1 or 2 indicating the class which the feature is influencing, for each non-zero feature

• levels a character vector of the affinity class labels

• subset a logical vector indicating whether the document was included in the computation of influence; FALSE for documents assigned a class label in training the model

• support logical vector for each feature matching the same return from predict.textmodel_affinity

See Also

influence.lm()

Examples

tmod <- textmodel_affinity(quanteda::data_dfm_lbgexample, y = c("L", NA, NA, NA, "R", NA))
pred <- predict(tmod)
influence(pred)

quanteda.textmodels documentation built on Sept. 11, 2024, 8:19 p.m.